Instructions to use Andyrasika/avatar_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Andyrasika/avatar_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Andyrasika/avatar_diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Transformers
How to use Andyrasika/avatar_diffusion with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Andyrasika/avatar_diffusion", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
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Parent(s): 26cc356
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README.md
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---
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license: creativeml-openrail-m
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library_name:
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pipeline_tag: text-to-image
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language:
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- en
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This text-to-image stable diffusion model was trained with dreambooth.
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Put in a text prompt and generate your own Avatar style image!
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```
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from diffusers import DiffusionPipeline, UniPCMultistepScheduler
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import torch
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---
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license: creativeml-openrail-m
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library_name: diffusers
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pipeline_tag: text-to-image
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language:
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- en
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This text-to-image stable diffusion model was trained with dreambooth.
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Put in a text prompt and generate your own Avatar style image!
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```
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from diffusers import DiffusionPipeline, UniPCMultistepScheduler
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import torch
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